Overview

Dataset statistics

Number of variables16
Number of observations7124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory890.6 KiB
Average record size in memory128.0 B

Variable types

NUM16

Warnings

Lunation Number is highly correlated with Year and 1 other fieldsHigh correlation
Year is highly correlated with Lunation Number and 1 other fieldsHigh correlation
Saros Number is highly correlated with Year and 1 other fieldsHigh correlation
Latitude is highly correlated with GammaHigh correlation
Gamma is highly correlated with LatitudeHigh correlation
Lunation Number has unique values Unique
Hour has 309 (4.3%) zeros Zeros
Minute has 132 (1.9%) zeros Zeros
Second has 109 (1.5%) zeros Zeros
Sun Altitude has 2558 (35.9%) zeros Zeros

Reproduction

Analysis started2020-12-18 20:56:50.285706
Analysis finished2020-12-18 20:57:32.691404
Duration42.41 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3000
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1505.684166
Minimum1
Maximum3000
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:32.790261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile144.15
Q1748
median1510.5
Q32261
95-th percentile2858
Maximum3000
Range2999
Interquartile range (IQR)1513

Descriptive statistics

Standard deviation868.656401
Coefficient of variation (CV)0.5769180685
Kurtosis-1.199475048
Mean1505.684166
Median Absolute Deviation (MAD)756.5
Skewness-0.004438847944
Sum10726494
Variance754563.9429
MonotocityIncreasing
2020-12-19T02:27:32.930847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
290450.1%
 
14850.1%
 
1850.1%
 
270950.1%
 
8350.1%
 
220650.1%
 
283950.1%
 
125550.1%
 
66950.1%
 
193550.1%
 
Other values (2990)707499.3%
 
ValueCountFrequency (%) 
12< 0.1%
 
22< 0.1%
 
33< 0.1%
 
42< 0.1%
 
52< 0.1%
 
ValueCountFrequency (%) 
30002< 0.1%
 
29992< 0.1%
 
29982< 0.1%
 
29972< 0.1%
 
29963< 0.1%
 

Month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.525828186
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:33.069953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.453594156
Coefficient of variation (CV)0.5292192895
Kurtosis-1.210904282
Mean6.525828186
Median Absolute Deviation (MAD)3
Skewness-0.01141406359
Sum46490
Variance11.92731259
MonotocityNot monotonic
2020-12-19T02:27:33.194918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
16098.5%
 
126078.5%
 
56058.5%
 
106048.5%
 
76038.5%
 
36018.4%
 
86018.4%
 
95908.3%
 
115888.3%
 
45858.2%
 
Other values (2)113115.9%
 
ValueCountFrequency (%) 
16098.5%
 
25517.7%
 
36018.4%
 
45858.2%
 
56058.5%
 
ValueCountFrequency (%) 
126078.5%
 
115888.3%
 
106048.5%
 
95908.3%
 
86018.4%
 

Day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.73133071
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:33.319890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.794962431
Coefficient of variation (CV)0.5590730112
Kurtosis-1.196098239
Mean15.73133071
Median Absolute Deviation (MAD)8
Skewness0.005765245367
Sum112070
Variance77.35136417
MonotocityNot monotonic
2020-12-19T02:27:33.444818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
252613.7%
 
42563.6%
 
152553.6%
 
32543.6%
 
262523.5%
 
162483.5%
 
212473.5%
 
122473.5%
 
222433.4%
 
112403.4%
 
Other values (21)462164.9%
 
ValueCountFrequency (%) 
12363.3%
 
22062.9%
 
32543.6%
 
42563.6%
 
52343.3%
 
ValueCountFrequency (%) 
311381.9%
 
302102.9%
 
292263.2%
 
282173.0%
 
272203.1%
 

Hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.41633914
Minimum0
Maximum23
Zeros309
Zeros (%)4.3%
Memory size55.7 KiB
2020-12-19T02:27:33.683085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.912594212
Coefficient of variation (CV)0.6055000758
Kurtosis-1.207951523
Mean11.41633914
Median Absolute Deviation (MAD)6
Skewness0.001828924562
Sum81330
Variance47.78395874
MonotocityNot monotonic
2020-12-19T02:27:33.808059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
93434.8%
 
173334.7%
 
53254.6%
 
43214.5%
 
203214.5%
 
13194.5%
 
133184.5%
 
213144.4%
 
183114.4%
 
03094.3%
 
Other values (14)391054.9%
 
ValueCountFrequency (%) 
03094.3%
 
13194.5%
 
22884.0%
 
32693.8%
 
43214.5%
 
ValueCountFrequency (%) 
232493.5%
 
223054.3%
 
213144.4%
 
203214.5%
 
192453.4%
 

Minute
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.57411567
Minimum0
Maximum59
Zeros132
Zeros (%)1.9%
Memory size55.7 KiB
2020-12-19T02:27:33.948647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median30
Q345
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.48673859
Coefficient of variation (CV)0.5912852573
Kurtosis-1.216235872
Mean29.57411567
Median Absolute Deviation (MAD)15
Skewness-0.00417108084
Sum210686
Variance305.7860265
MonotocityNot monotonic
2020-12-19T02:27:34.089528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
171492.1%
 
551462.0%
 
361432.0%
 
41402.0%
 
341381.9%
 
591371.9%
 
461361.9%
 
481351.9%
 
541321.9%
 
01321.9%
 
Other values (50)573680.5%
 
ValueCountFrequency (%) 
01321.9%
 
11201.7%
 
21171.6%
 
31131.6%
 
41402.0%
 
ValueCountFrequency (%) 
591371.9%
 
581171.6%
 
571121.6%
 
561191.7%
 
551462.0%
 

Second
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.43879843
Minimum0
Maximum59
Zeros109
Zeros (%)1.5%
Memory size55.7 KiB
2020-12-19T02:27:34.230120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median29
Q344
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.21196165
Coefficient of variation (CV)0.5846692993
Kurtosis-1.174586653
Mean29.43879843
Median Absolute Deviation (MAD)15
Skewness0.01912891729
Sum209722
Variance296.2516238
MonotocityNot monotonic
2020-12-19T02:27:34.386333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
231422.0%
 
81381.9%
 
401361.9%
 
311351.9%
 
221341.9%
 
251341.9%
 
391301.8%
 
581301.8%
 
421301.8%
 
161301.8%
 
Other values (50)578581.2%
 
ValueCountFrequency (%) 
01091.5%
 
11191.7%
 
21231.7%
 
31181.7%
 
41141.6%
 
ValueCountFrequency (%) 
591091.5%
 
581301.8%
 
571211.7%
 
561241.7%
 
551291.8%
 

Delta T (s)
Real number (ℝ)

Distinct4430
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2623.782847
Minimum-6
Maximum10520
Zeros6
Zeros (%)0.1%
Memory size55.7 KiB
2020-12-19T02:27:34.532702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile12
Q1331.75
median1488.5
Q33955.25
95-th percentile9124.4
Maximum10520
Range10526
Interquartile range (IQR)3623.5

Descriptive statistics

Standard deviation2874.150626
Coefficient of variation (CV)1.095422447
Kurtosis0.3402944201
Mean2623.782847
Median Absolute Deviation (MAD)1382.5
Skewness1.184776389
Sum18691829
Variance8260741.819
MonotocityNot monotonic
2020-12-19T02:27:34.670770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12701.0%
 
10520.7%
 
16430.6%
 
7430.6%
 
9420.6%
 
24420.6%
 
8410.6%
 
11390.5%
 
-6340.5%
 
17330.5%
 
Other values (4420)668593.8%
 
ValueCountFrequency (%) 
-6340.5%
 
-5130.2%
 
-4100.1%
 
-340.1%
 
-250.1%
 
ValueCountFrequency (%) 
105201< 0.1%
 
105151< 0.1%
 
105101< 0.1%
 
105051< 0.1%
 
105001< 0.1%
 

Lunation Number
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct7124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6107.755474
Minimum-24719
Maximum12378
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:34.826977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-24719
5-th percentile-22945.25
Q1-15476.5
median-6047
Q33230.25
95-th percentile10616.25
Maximum12378
Range37097
Interquartile range (IQR)18706.75

Descriptive statistics

Standard deviation10743.78181
Coefficient of variation (CV)-1.759039283
Kurtosis-1.199457299
Mean-6107.755474
Median Absolute Deviation (MAD)9355
Skewness-0.004451505483
Sum-43511650
Variance115428847.6
MonotocityStrictly increasing
2020-12-19T02:27:35.014395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
81881< 0.1%
 
-75991< 0.1%
 
34751< 0.1%
 
-55281< 0.1%
 
-116731< 0.1%
 
75771< 0.1%
 
116791< 0.1%
 
-137321< 0.1%
 
-198771< 0.1%
 
99951< 0.1%
 
Other values (7114)711499.9%
 
ValueCountFrequency (%) 
-247191< 0.1%
 
-247131< 0.1%
 
-247071< 0.1%
 
-247011< 0.1%
 
-246951< 0.1%
 
ValueCountFrequency (%) 
123781< 0.1%
 
123721< 0.1%
 
123661< 0.1%
 
123601< 0.1%
 
123551< 0.1%
 

Saros Number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct142
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.8802639
Minimum49
Maximum190
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:35.187060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile70
Q195
median120
Q3145
95-th percentile170
Maximum190
Range141
Interquartile range (IQR)50

Descriptive statistics

Standard deviation31.00446922
Coefficient of variation (CV)0.2586286367
Kurtosis-0.8591954257
Mean119.8802639
Median Absolute Deviation (MAD)25
Skewness0.009622176535
Sum854027
Variance961.2771115
MonotocityNot monotonic
2020-12-19T02:27:35.327653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
127821.2%
 
109811.1%
 
129801.1%
 
111791.1%
 
147771.1%
 
146761.1%
 
145761.1%
 
108761.1%
 
90751.1%
 
148751.1%
 
Other values (132)634789.1%
 
ValueCountFrequency (%) 
492< 0.1%
 
5060.1%
 
5160.1%
 
5290.1%
 
53130.2%
 
ValueCountFrequency (%) 
1901< 0.1%
 
18950.1%
 
18870.1%
 
18780.1%
 
186120.2%
 

Eclipse Type
Real number (ℝ≥0)

Distinct19
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.392897249
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:35.452617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median11
Q314
95-th percentile14
Maximum19
Range18
Interquartile range (IQR)13

Descriptive statistics

Standard deviation5.435121157
Coefficient of variation (CV)0.6475858093
Kurtosis-1.50227774
Mean8.392897249
Median Absolute Deviation (MAD)3
Skewness-0.4438260045
Sum59791
Variance29.54054199
MonotocityNot monotonic
2020-12-19T02:27:35.575803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
11230332.3%
 
1228032.0%
 
14184525.9%
 
72603.6%
 
131001.4%
 
12971.4%
 
4450.6%
 
17450.6%
 
3250.4%
 
5230.3%
 
Other values (9)1011.4%
 
ValueCountFrequency (%) 
1228032.0%
 
2200.3%
 
3250.4%
 
4450.6%
 
5230.3%
 
ValueCountFrequency (%) 
1970.1%
 
1880.1%
 
17450.6%
 
162< 0.1%
 
15110.2%
 

Gamma
Real number (ℝ)

HIGH CORRELATION

Distinct6340
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.001531471084
Minimum-1.569
Maximum1.5706
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:35.812988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1.569
5-th percentile-1.390635
Q1-0.781475
median-0.005
Q30.7716
95-th percentile1.394655
Maximum1.5706
Range3.1396
Interquartile range (IQR)1.553075

Descriptive statistics

Standard deviation0.8990261263
Coefficient of variation (CV)-587.0343462
Kurtosis-1.211814132
Mean-0.001531471084
Median Absolute Deviation (MAD)0.7766
Skewness0.001172210511
Sum-10.9102
Variance0.8082479757
MonotocityNot monotonic
2020-12-19T02:27:35.969201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.005840.1%
 
-0.499140.1%
 
0.25563< 0.1%
 
-0.82133< 0.1%
 
0.17633< 0.1%
 
0.85163< 0.1%
 
1.31573< 0.1%
 
-0.00113< 0.1%
 
0.74133< 0.1%
 
0.59843< 0.1%
 
Other values (6330)709299.6%
 
ValueCountFrequency (%) 
-1.5691< 0.1%
 
-1.56741< 0.1%
 
-1.56591< 0.1%
 
-1.56251< 0.1%
 
-1.55881< 0.1%
 
ValueCountFrequency (%) 
1.57061< 0.1%
 
1.5651< 0.1%
 
1.55871< 0.1%
 
1.55811< 0.1%
 
1.55691< 0.1%
 

Eclipse Magnitude
Real number (ℝ≥0)

Distinct3654
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8145370578
Minimum0.0003
Maximum1.0813
Zeros0
Zeros (%)0.0%
Memory size55.7 KiB
2020-12-19T02:27:36.109793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0003
5-th percentile0.14105
Q10.68535
median0.9507
Q31.02
95-th percentile1.0623
Maximum1.0813
Range1.081
Interquartile range (IQR)0.33465

Descriptive statistics

Standard deviation0.2984566211
Coefficient of variation (CV)0.3664125753
Kurtosis0.3869263555
Mean0.8145370578
Median Absolute Deviation (MAD)0.0854
Skewness-1.324326552
Sum5802.762
Variance0.08907635466
MonotocityNot monotonic
2020-12-19T02:27:36.267852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9422100.1%
 
1.0475100.1%
 
0.9506100.1%
 
1.037190.1%
 
1.046490.1%
 
0.947490.1%
 
1.006590.1%
 
1.038890.1%
 
1.049590.1%
 
0.948190.1%
 
Other values (3644)703198.7%
 
ValueCountFrequency (%) 
0.00031< 0.1%
 
0.00091< 0.1%
 
0.0011< 0.1%
 
0.00131< 0.1%
 
0.00191< 0.1%
 
ValueCountFrequency (%) 
1.08131< 0.1%
 
1.08121< 0.1%
 
1.08111< 0.1%
 
1.0811< 0.1%
 
1.08071< 0.1%
 

Latitude
Real number (ℝ)

HIGH CORRELATION

Distinct1542
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1020494104
Minimum-88.5
Maximum89.5
Zeros2
Zeros (%)< 0.1%
Memory size55.7 KiB
2020-12-19T02:27:36.408448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-88.5
5-th percentile-71
Q1-51.3
median0.4
Q352
95-th percentile70.9
Maximum89.5
Range178
Interquartile range (IQR)103.3

Descriptive statistics

Standard deviation50.73545894
Coefficient of variation (CV)497.1656251
Kurtosis-1.423050035
Mean0.1020494104
Median Absolute Deviation (MAD)51.6
Skewness-0.008307880137
Sum727
Variance2574.086793
MonotocityNot monotonic
2020-12-19T02:27:36.549043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-61430.6%
 
-61.2360.5%
 
61.2360.5%
 
-61.1350.5%
 
-71.9340.5%
 
61.1340.5%
 
61340.5%
 
61.3310.4%
 
-61.3310.4%
 
60.9310.4%
 
Other values (1532)677995.2%
 
ValueCountFrequency (%) 
-88.51< 0.1%
 
-881< 0.1%
 
-87.52< 0.1%
 
-87.21< 0.1%
 
-86.91< 0.1%
 
ValueCountFrequency (%) 
89.51< 0.1%
 
88.11< 0.1%
 
87.62< 0.1%
 
87.31< 0.1%
 
86.41< 0.1%
 

Longitude
Real number (ℝ)

Distinct3073
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.285612016
Minimum-179.9
Maximum180
Zeros1
Zeros (%)< 0.1%
Memory size55.7 KiB
2020-12-19T02:27:36.691668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-179.9
5-th percentile-160.8
Q1-89.1
median1.6
Q391.625
95-th percentile162.7
Maximum180
Range359.9
Interquartile range (IQR)180.725

Descriptive statistics

Standard deviation104.0035211
Coefficient of variation (CV)80.8980624
Kurtosis-1.20238398
Mean1.285612016
Median Absolute Deviation (MAD)90.5
Skewness-0.005296028316
Sum9158.7
Variance10816.7324
MonotocityNot monotonic
2020-12-19T02:27:36.832298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
133.980.1%
 
-173.370.1%
 
27.670.1%
 
-1570.1%
 
58.870.1%
 
-123.370.1%
 
2770.1%
 
160.470.1%
 
121.270.1%
 
-100.770.1%
 
Other values (3063)705399.0%
 
ValueCountFrequency (%) 
-179.93< 0.1%
 
-179.82< 0.1%
 
-179.62< 0.1%
 
-179.52< 0.1%
 
-179.43< 0.1%
 
ValueCountFrequency (%) 
1802< 0.1%
 
179.91< 0.1%
 
179.81< 0.1%
 
179.63< 0.1%
 
179.43< 0.1%
 

Sun Altitude
Real number (ℝ≥0)

ZEROS

Distinct91
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.58043234
Minimum0
Maximum90
Zeros2558
Zeros (%)35.9%
Memory size55.7 KiB
2020-12-19T02:27:36.972893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39
Q366
95-th percentile86
Maximum90
Range90
Interquartile range (IQR)66

Descriptive statistics

Standard deviation32.41111326
Coefficient of variation (CV)0.8860232421
Kurtosis-1.537757184
Mean36.58043234
Median Absolute Deviation (MAD)38
Skewness0.1133346904
Sum260599
Variance1050.480263
MonotocityNot monotonic
2020-12-19T02:27:37.113483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0255835.9%
 
74941.3%
 
87881.2%
 
79861.2%
 
86841.2%
 
78841.2%
 
65831.2%
 
60821.2%
 
89811.1%
 
88811.1%
 
Other values (81)380353.4%
 
ValueCountFrequency (%) 
0255835.9%
 
12< 0.1%
 
23< 0.1%
 
360.1%
 
480.1%
 
ValueCountFrequency (%) 
90470.7%
 
89811.1%
 
88811.1%
 
87881.2%
 
86841.2%
 

Sun Azimuth
Real number (ℝ≥0)

Distinct361
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.5254071
Minimum0
Maximum360
Zeros14
Zeros (%)0.2%
Memory size55.7 KiB
2020-12-19T02:27:37.271805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q189
median181
Q3273
95-th percentile349
Maximum360
Range360
Interquartile range (IQR)184

Descriptive statistics

Standard deviation110.871035
Coefficient of variation (CV)0.614157513
Kurtosis-1.091089972
Mean180.5254071
Median Absolute Deviation (MAD)92
Skewness-0.00540121851
Sum1286063
Variance12292.38641
MonotocityNot monotonic
2020-12-19T02:27:37.422018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
198711.0%
 
18711.0%
 
161570.8%
 
162570.8%
 
209550.8%
 
151550.8%
 
331550.8%
 
343530.7%
 
29530.7%
 
342520.7%
 
Other values (351)654591.9%
 
ValueCountFrequency (%) 
0140.2%
 
1300.4%
 
2290.4%
 
3350.5%
 
4340.5%
 
ValueCountFrequency (%) 
360200.3%
 
359320.4%
 
358320.4%
 
357310.4%
 
356360.5%
 

Interactions

2020-12-19T02:26:51.455701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:26:51.654230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-19T02:27:22.360751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-19T02:27:22.598225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-19T02:27:25.702771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:27:25.827736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:27:25.952709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:27:26.091583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:27:26.204419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-19T02:27:26.582161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-19T02:27:32.192263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-19T02:27:37.563672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-19T02:27:37.881937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-19T02:27:38.085049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-19T02:27:38.273829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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2020-12-19T02:27:32.605002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

YearMonthDayHourMinuteSecondDelta T (s)Lunation NumberSaros NumberEclipse TypeGammaEclipse MagnitudeLatitudeLongitudeSun AltitudeSun Azimuth
016106441610520-2471966140.36251.061744.4121.269179
111232053810515-24713711-0.03970.9558-24.7-79.2883
2253019454310510-24707767-0.41681.0087-3.1-72.565356
321123561510505-247018170.64761.007420.0149.549187
435201413010500-246958611-1.23190.5663-69.0-139.30337
5310149215910496-246905311-1.38040.2914-71.4-32.10114
63111219362910495-2468991111.28220.477269.6-41.20211
744811391410491-246845810.86470.948661.716.830142
8410223522510486-24678637-0.73681.0095-47.8-156.34227
9532817233410481-246726870.07891.00226.6-37.285162

Last rows

YearMonthDayHourMinuteSecondDelta T (s)Lunation NumberSaros NumberEclipse TypeGammaEclipse MagnitudeLatitudeLongitudeSun AltitudeSun Azimuth
71142996762344343951232516210.50130.950851.6-145.660199
71152996123112581743991233116714-0.17291.0249-32.96.280349
71162997626341444403123371721-0.27930.99167.2141.9748
71172997122023451544061234317710.54490.96969.6-161.957175
7118299861514492744101234918211-1.01580.9792-66.5-32.509
71192998121031831441412355187111.28380.477367.2145.00179
7120299956232357441712360154140.83881.056671.5177.333146
712129991030934334420123661592-1.00230.9586-70.9-84.70137
7122300042614186442412372164140.13101.022221.1-18.482166
7123300010191610164428123781697-0.23031.0049-23.1-51.67716